applying function to multiple dataframes programatically [duplicate] - r

Question

I am new to R, and this is a very simple question. I've found a lot of similar things to what I want but not exactly it. Basically I have multiple data frames and I simply want to run the same function across all of them. A for-loop could work but I'm not sure how to set it up properly to call data frames. It also seems most prefer the lapply approach with R. I've played with the get function as well to no avail. I apologize if this is a duplicated question. Any help would be greatly appreciated!
Here's my over simplified example:
2 data frames: df1, df2
df1
start stop ID
0 10 x
10 20 y
20 30 z
df2
start stop ID
0 10 a
10 20 b
20 30 c
what I want is a 4th column with the average of start and stop for both dfs
df1
start stop ID Avg
0 10 x 5
10 20 y 15
20 30 z 25
I can do this one data frame at a time with:
df1$Avg <- rowMeans(subset(df1, select = c(start, stop)), na.rm = TRUE)
but I want to run it on all of the dataframes.

Answer 1

Make a list of data frames then use lapply to apply the function to them all.
df.list <- list(df1,df2,...)
res <- lapply(df.list, function(x) rowMeans(subset(x, select = c(start, stop)), na.rm = TRUE))
# to keep the original data.frame also
res <- lapply(df.list, function(x) cbind(x,"rowmean"=rowMeans(subset(x, select = c(start, stop)), na.rm = TRUE)))
The lapply will then feed in each data frame as x sequentially.

Answer 2

Put them into a list and then run rowMeans over the list.
df1 <- data.frame(x = rep(3, 5), y = seq(1, 5, 1), ID = letters[1:5])
df2 <- data.frame(x = rep(5, 5), y = seq(2, 6, 1), ID = letters[6:10])
lapply(list(df1, df2), function(w) { w$Avg <- rowMeans(w[1:2]); w })
[[1]]
x y ID Avg
1 3 1 a 2.0
2 3 2 b 2.5
3 3 3 c 3.0
4 3 4 d 3.5
5 3 5 e 4.0
[[2]]
x y ID Avg
1 5 2 f 3.5
2 5 3 g 4.0
3 5 4 h 4.5
4 5 5 i 5.0
5 5 6 j 5.5

Answer 3

In case you want all the outputs in the same file this may help.
df1 <- data.frame(x = rep(3, 5), y = seq(1, 5, 1), ID = letters[1:5])
df2 <- data.frame(x = rep(5, 5), y = seq(2, 6, 1), ID = letters[6:10])
z=list(df1,df2)
df=NULL
for (i in z) {
i$Avg=(i$x+i$y)/2
df<-rbind(df,i)
print (df)
}
> df
x y ID Avg
1 3 1 a 2.0
2 3 2 b 2.5
3 3 3 c 3.0
4 3 4 d 3.5
5 3 5 e 4.0
6 5 2 f 3.5
7 5 3 g 4.0
8 5 4 h 4.5
9 5 5 i 5.0
10 5 6 j 5.5

Answer 4

Here's another possible solution using a for loop. I've had the same problem (with more datasets) a few days ago and other solutions did not work.
Say you have n datasets :
df1 <- data.frame(start = seq(0,20,10), stop = seq(10,30,10), ID = letters[24:26])
df2 <- data.frame(start = seq(0,20,10), stop = seq(10,30,10), ID = letters[1:3])
...
dfn <- data.frame(start = seq(0,20,10), stop = seq(10,30,10), ID = letters[n:n+2])
The first thing to do is to make a list of the dfs:
df.list<-lapply(1:n, function(x) eval(parse(text=paste0("df", x)))) #In order to store all datasets in one list using their name
names(df.list)<-lapply(1:n, function(x) paste0("df", x)) #Adding the name of each df in case you want to unlist the list afterwards
Afterwards, you can use the for loop (that's the most important part):
for (i in 1:length(df.list)) {
df.list[[i]][["Avg"]]<-rowMeans(df.list[[i]][1:2])
}
And you have (in the case your list only includes the two first datasets):
> df.list
[[1]]
start stop ID Avg
1 0 10 x 5
2 10 20 y 15
3 20 30 z 25
[[2]]
start stop ID Avg
1 0 10 a 5
2 10 20 b 15
3 20 30 c 25
Finally, if you want your modified datasets from the list back in the global environment, you can do:
list2env(df.list,.GlobalEnv)
This technique can be applied to n datasets and other functions.
I find it to be the most flexible solution.